Sample Size Determination for Health Psychology Interventions with Binomially Distributed Outcomes

Abstract

Health intervention outcomes are often assessed as binomially distributed variables. In designing such interventions it is important to model the pre-intervention rate of the target behavior when performing sample size calculations. Unfortunately, the majority of sample size programs model post-intervention outcomes only, which results in exaggerated sample size estimates. An exception is Yoo and Spoth's (1993) conditional binomial method of sample size determination. This approach explicitly models pre-intervention behavior by focusing on baserate-adjusted post-intervention outcomes, and always results in smaller sample size estimates than conventional approaches. Advantages of the conditional binomial method are discussed and user-friendly software is presented.